基于分块的2DPCA人脸识别方法

将分块理论与2DPCA方法相结合,研究分块二维主成分分析法（M-2DPCA）在人脸识别中的应用.对人脸图像矩阵进行分块,用形成的子图像矩阵直接构造总体散布矩阵并求解对应的特征向量,利用提取的特征向量对图像进行特征的提取与分析,进行人脸识别.基于Yale人脸数据库的实验显示,在相同训练样本和特征向量条件下,M-2DPCA...

A M-2DPCA Face Recognition Method

The block theory and two- dimensional principal component analysis （2DPCA） were combined, and the modular two- di- mensional principal component analysis （M -2DPCA） was studied in face recognition. The original image matrix was divided into modu- lar image matrixes, and the image covariance matrix was formed directly by using sub - image matrixes, and its eigenvectors were de- rived. The eigenvectors were used to extract and analyze image feature for face recognition. The experiments based on the Yale face data- base showed that it had a higher recognition rate of M -2DPCA than 2DPCA under the same training specimens and eigenvectors. The information of image covariance matrix was fully utilized in M - 2DPCA method, which had an admirable recognition rate and robustness on face recognition, and it was important to further research on face recognition.